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The overall aim of the course is to provide you with a broad and flexible education on the key methodologies in decision making within management across a range of areas within industry, the finance sector, government. The emphasis is on equipping you with the insight and ability to adapt to an ever developing field, rather than an in depth knowledge of specific methods and packages.

Teaching and AssessmentFor the taught component of the MSc, you will study on a number of core modules that will introduce you to the key methodologies of the decision sciences. This will involve a combination of lectures and problem classes where you will learn about the details of the methods and then apply them to conventional, and then less conventional, problems.

In this MSc there will be significant emphasis on you solving problems. The aim of the course is, as well as to impart a lot of technical expertise, to allow you to think and respond flexibly to a problem. A comfort in using a range of methods is important, and for this practice is vital. This will be provided by supporting problem classes, and also a significant amount of extra study based upon problems.

Career ProspectsThe decision making process is central to modern management, and the course will equip graduates to work in a variety of areas which require significant quantitative and computational expertise such as Credit Risk, Project Management, Information Systems and Supply Chain and Operations Management, as well as being of more generic value in such areas as understanding and managing risk and organisational behaviour. Decision Science Analyst is a common job title in a large range of organisations.

Assessment on core modules will be a mixture of coursework and written examinations. The major component of assessment on most modules (typically worth 70% of the marks) will be a final written examination which will assess your overall understanding of the module.

Contents

The course consists of a combination of core modules and a single optional stream, which you can select based upon your interests. The core modules show how a variety of mathematical and related methods can be used for decision making. Example subject streams are Economics, and Systems and Control Engineering.

Course StructureCore modules:

Managing risk and uncertainty

The management of uncertainty, its quantification and consequences are central to the management of long term and capital projects. The evaluation of hazards and contingencies based on the objective and subjective evidence provide the basis for risk sharing between supplier and client. The module will emphasise the role of uncertainty and the management of uncertainty and risk in business, engineering, science and decision making.

Optimisation and Decision Making

Most problems require a decision to be made, usually the determination of "the best" choice in some sense. Technically such a question is formulated as a mathematical optimization problem. This module concentrates on the translation of decision problems into an appropriate mathematical form and the solution of the resulting optimization problem. The module covers linear and non-linear optimization for both deterministic and non-deterministic problems.

Game Theory

Game theory is the science of decision making where optimal decisions are affected by the actions of others, so the more straightforward optimization methods do not work. This module concentrates on methods of how to select strategies in different concepts, including dominance methods and backwards induction, and the important concept of the Nash equilibrium. In addition we consider games with asymmetric information, as well as cooperative games.

Agent-based modelling

Agent-based modelling is the computational modelling of a population of individuals, where each individual follows a particular type of behaviour. It is a general methodology that can be applied in a wide range of areas, including ecology, collective animal behaviour, economics, sociology, and many other sciences. This module will consider how to construct an agent-based model, what factors make a good model and look at some instructive examples.

Evolutionary Game Theory

Evolutionary game theory is the application of game theoretical ideas to the study of animal populations, and how they evolve. This module covers the important static and dynamic models from the theoretical point of view and how they predict key features of populations, such as how cooperation can evolve, and when violent interactions should or should not occur. Game theory originated in Economics before being applied to biology, and now evolutionary game models are also applied to Economic situations, and this will also be discussed.

Requirements

University requirements

Program requirements

Applicants should normally have a good (first or upper second class, or equivalent) first degree in a subject with a sound basis in Mathematics, such as Mathematics, Physics, Engineering and Economics.Students who have previously studied a degree taught in a language other than English will be required to have one of the following qualifications: * IELTS: 6.5 * TOEFL (internet based): 90 English Language Requirements IELTS band: 6.5 TOEFL iBT® test: 90 IMPORTANT NOTE: Since April 2014 the ETS tests (including TOEFL and TOEIC) are no longer accepted for Tier 4 visa applications to the United Kingdom. The university might still accept these tests to admit you to the university, but if you require a Tier 4 visa to enter the UK and begin your degree programme, these tests will not be sufficient to obtain your Visa. The IELTS test is most widely accepted by universities and is also accepted for Tier 4 visas to the UK- learn more.